Discipline: Chemistry and Chemical Sciences
Subcategory: Biomedical Engineering
Maritza Zaldivar-Lima - University of Washington
Co-Author(s): Ian Faulkner, University of Washington; Department of Chemical Engineering and Bioengineering; Molecular Engineering and Sciences Institute; Washington ; James Carothers, University of Washington; Department of Chemical Engineering and Bioengineering; Molecular Engineering and Sciences Institute; Center for Synthetic Biology, Washington
The expression of genetic circuits can burden cells and reduce their ability to express additional transcription and sensory factors without causing toxicities that can create high rates of variance and leakage in expression within the circuit. Expression from individual ‘channels’ within a dual T7 RNA polymerase circuit design can be modeled as an input-output transfer function, allowing us to quantify the toxicity of the T7B transcription factor in the circuit. Quantifying the toxicity can indicate the reaction in fluorescent proteins compared the amount of induction produced. In this work, genetic circuits were designed, built and then tested in two different strains of E. coli (MG1655 and BL21). Expression of the T7B gene from the pTet promoter was induced by the addition of aTc. The resulting expression and growth data were assembled into a Comma-Separated-Value (CSV) file and evaluated using a python program that produces Hill functions for visualization. Channel B exhibited good induction properties that could be fit to a Hill function. Our data show that circuits expressed in BL21 cells needed less transcription, and exhibited less burden to achieve the same level of circuit output as the same circuit expressed in MG1655 cells.Not Submitted
Funder Acknowledgement(s): University of Washington GenOM Project (NIH 5R25HG007153-05). University of Washington Office of Research ; This work was supported in part by funds from NSF Award MCB 1517052 to J.M.C.
Faculty Advisor: James Carothers, firstname.lastname@example.org
Role: I developed a Python program that allowed the experimental data to be read and fitted for a Hill Function graph. Outside of compiling the data, I did strain building and analysis as well as DNA testing in a few of the trials. This consisted of PCR, CPEC, cell transformations, cell inductions as well as gel electrophoresis.